We report improvements on automatic continuous sleep staging using
Hidden Markov Models (HMM). Our totally unsupervised approach
detects the cornerstones of human sleep (wakefulness, deep and rem
sleep) with around 80% accuracy based on data from a single EEG
channel. Contrary to our previous efforts we trained the HMM on data
from a single sleep lab instead of generalizing to data from diverse
sleep labs. This solved our previous problem of detecting rem sleep.